Magnetic resonance images (MRI) acquired with similar protocols but on different scanners will show dissimilar intensity contrasts for the same tissue types. These variations are machine-dependant, and go beyond random or systematic errors that can be corrected with image de-noising that are known in the art or bias field heterogeneity estimation. This situation is particularly acute in large, multi-centric settings such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), in which data was acquired from 56 different centers in the United States and Canada. The ADNI was launched in 2003 by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, the Food and Drug Administration, private pharmaceutical companies and non-profit organizations, as a $60-million, 5-year public-private partnership. It collected data on more than 800 subjects for Alzheimer's neuroimaging research.
Automated image-processing pipelines must be robust to these variations, if they are to provide reliable and reproducible measurements that have clinical meaning. Thus, intensity standardization must be performed so that similar intensities will have similar tissue meaning in the standardized images, regardless of scanner origin, location, type or operator. Techniques exist to perform standardization, but they are essentially aimed at matching the image histogram (i.e. from the image to be standardized) onto a standard or reference image histogram In particular, the technique of Nyul et al. [Nyul, L. G., J. K. Udupa, and X. Zhang, New variants of a method of MRI scale standardization. IEEE Trans Med Imaging, 2000. 19(2): p. 143-50.] matches percentile histogram landmarks (PCT), linearly interpolating intensities between them. Applicant's experience dictated that histogram matching should not be considered the unique objective, as it may artificially distort image contrasts and therefore result in a loss of biological meaning, quite exactly the opposite effect sought after. In some cases, two different tissue types can have a similar intensity profiles and therefore provide inefficient intensity adjustment and/or intensity adjustments that are not adapted to the specific tissue type. Indeed, intensity values alone do not inherently carry information about the tissue being observed. Rather, standardization should be aimed at matching spatially corresponding tissue intensities to remove, as much as possible, scanner effects. FIG. 1 shows a flowchart of a prior art methods for standardization.
One of the drawbacks of the prior art methods is that, in some cases, two different tissue types can have a similar intensity profiles (for example CSF and background), and therefore provide inefficient intensity adjustment and/or intensity adjustments that are not adapted to the specific tissue type.